Advancements in data mining algorithms have significantly progressed the discovery of complex patterns within biological sequences, aiding in understanding genetic bases and detecting MOTIFS. While these algorithms have been extensively applied to general biological data, a research gap exists in the sequential pattern mining of fungal proteomic sequences. This study addresses this gap by evaluating the performance of three key algorithms—MFPS, FP-Max, and BIDE—focusing on their efficiency in mining maximal and closed sequential patterns from fungal proteomic data. We selected these algorithms for their ability to provide a compact representation of data, aiming to compare their performance in terms of runtime (the time taken to execute the algorithm), memory usage (the amount of computer memory required to run the algorithm), and the quantity and length of patterns identified. Using a dataset of Lachancea meyersii CBS 8951, we conducted experiments with various minimum support thresholds to measure each algorithm's efficiency and resource consumption. Our findings highlight the trade-offs between time and memory consumption across different algorithms. MFPS was found to be time-intensive but mid-term in memory usage. FPMAX used mid-term execution times and mid-time consumption memory. BIDE demonstrated faster execution times but higher memory usage. This comparative analysis not only provides insights into the suitability of maximal and closed sequential pattern mining algorithms for fungal proteomic sequences but also directly guides researchers in selecting the most appropriate tools based on their specific needs and constraints. Future research could explore optimization strategies for these algorithms and extend their application to larger datasets, thereby enhancing their utility in computational biology and proteomics.